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This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

Since the beginning of the 21st century, China’s outward foreign direct investment (OFDI) is growing steadily and Chinese multinationals (MNCs) are playing an increasingly important role in the global economy. Thus, the number of papers focusing on China’s OFDI and Chinese MNCs has been increasing during the last years. The aim of this chapter is to carry out a review of the empirical papers dealing with Chinese MNCs published between 2002 and 2012 in high-impact international business and management journals.

This chapter reviews 43 empirical papers focusing on Chinese MNCs that were published in nine major scholarly journals between 2002 and 2012.

We report individual and institutional contributions, the theories and methods used, the research topics, and the main findings. We also discuss implications for future research.

Some previous literature reviews have dealt with research on China’s OFDI and Chinese MNCs. Nevertheless, none of the earlier reviews dealt specifically with empirical papers; neither did they provide an analysis of both individual and institutional contributions.

Proper platform pose is important for the mobile manipulator to accomplish dexterous manipulation tasks efficiently and safely, and the evaluation criterion to qualify manipulation performance is critical to support the pose decision process. This paper aims to present a comprehensive index to evaluate the manipulator’s operation performance from various aspects.

In this research, a criterion called hybrid manipulability (HM) is proposed to assess the performance of the manipulator’s operation, considering crucial factors such as joint limits, obstacle avoidance and stability. The determination of the optimal platform pose is achieved by selecting the pose that maximizes the HM within the feasible inverse reachability map associated with the target object.

A self-built mobile manipulator is adopted as the experimental platform, and the feasibility of the proposed method is experimentally verified in the context of object-grasping tasks both in simulation and practice.

The proposed HM extends upon the conventional notion of manipulability by incorporating additional factors, including the manipulator’s joint limits, the obstacle avoidance situation during the operation and the manipulation stability when grasping the target object. The manipulator can achieve enhanced stability during grasping when positioned in the pose determined by the HM.

The purpose of this paper is to find the key innovative principles for evaluating the long-term care (LTC) cloud system by exploring contradictory and complex points in its development.

The theory of inventive problem solving (TRIZ) and the decision-making trial and evaluation laboratory (DEMATEL) approaches are integrated to resolve complex contradictions in the system. The heuristic reasoning of TRIZ is applied to obtain innovation principles for an LTC cloud mining system. However, the importance and feasibility of these innovative principles require further assessment. In this study, DEMATEL is employed to clarify the complex relationships among the principles and evaluate their key influences.

This paper identifies six primary contradictions and derives 25 innovative principles for the resolution of these conflicts. Further analysis confirms three key innovative principles. First, the government should consider the overall planning of the cloud system platform, followed by the participation of other medical and LTC institutions. Second, the information capability of LTC institutions should be unified by recording the pathology data of care recipients to create an information exchange system. Third, LTC institutions should act in cooperation with medical institutions to provide professional medical capabilities.

The contributions of this paper are two-fold. First, this study provides an integrated methodology integrating the TRIZ and DEMATEL approaches to resolve LTC problems. Second, this research identifies the key innovative principles for developing an LTC cloud system in Taiwan.

During the production process of steel strip, some defects may appear on the surface, that is, traditional manual inspection could not meet the requirements of low-cost and high-efficiency production. The purpose of this paper is to propose a method of feature selection based on filter methods combined with hidden Bayesian classifier for improving the efficiency of defect recognition and reduce the complexity of calculation. The method can select the optimal hybrid model for realizing the accurate classification of steel strip surface defects.

A large image feature set was initially obtained based on the discrete wavelet transform feature extraction method. Three feature selection methods (including correlation-based feature selection, consistency subset evaluator [CSE] and information gain) were then used to optimize the feature space. Parameters for the feature selection methods were based on the classification accuracy results of hidden Naive Bayes (HNB) algorithm. The selected feature subset was then applied to the traditional NB classifier and leading extended NB classifiers.

The experimental results demonstrated that the HNB model combined with feature selection approaches has better classification performance than other models of defect recognition. Among the results of this study, the proposed hybrid model of CSE + HNB is the most robust and effective and of highest classification accuracy in identifying the optimal subset of the surface defect database.

The main contribution of this paper is the development of a hybrid model combining feature selection and multi-class classification algorithms for steel strip surface inspection. The proposed hybrid model is primarily robust and effective for steel strip surface inspection.

The purpose of this paper is to examine how global liquidity affects international housing prices. The data sample covers 35 economies from 2000Q1 to 2017Q4.

The existing papers seldom investigated whether the impacts of global liquidity on housing prices display differences between advanced and developing economies. Cesa-Bianchi et al. (2015) is an exceptional study in that they focused on the impulse response of house price volatility to global liquidity shocks but did not examine the long-run equilibrium relationship. To fill the gap in the existing research, this paper used panel cointegration of Pedroni (2000, 2004) to estimate the long-run linkage between global liquidity and housing prices in both advanced and developing economies, and generalized impulse response function (GIRF) and generalized variance decomposition (GVDC) were also applied to capture the relative strengths and contribution of global liquidity shock on house price volatility.

First, the global liquidity elasticity of housing prices is 0.0679 in developing economies, and 0.0454 in advanced economies, implying that the positive effect of global liquidity on housing prices is higher in developing economies. Next, the results of generalized impulse response indicate that the innovation of global liquidity can significantly and positively impact housing prices only in developing economies and the duration is two quarters. Third, in light of the long-run portions of the global liquidity shock on house price volatility in individual economies, the two highest portions are 28.51% in the USA and 20.04% in the UK, while there are low portions, less than 10%, in most of the European economies. Moreover, comparing the long-run contributions of global liquidity and other variables shock on house price volatility, the contribution of the global liquidity shock ranks the highest or second highest in 21 out of 35 economies, confirming that it played a more important role than other economic variables in explaining house price volatility for most economies.

Compared with the related literature, the contributions of this paper are as follows. First, except for Cesa-Bianchi et al. (2015), the existing papers seldom investigated whether the impacts of global liquidity on housing prices display differences between advanced and developing economies. Hence, the study adopted a wider data sample, including 7 developing economies and 28 advanced economies, to examine the differences in the impact of global liquidity on housing prices between advanced and developing economies. Second, most of the relative literature calculated global liquidity by applying a monetary aggregate, such as M2 or M3, while Cesa-Bianchi et al. (2015) argued that global liquidity being measured by the international supply of credit is intuitively connected to housing prices. This paper follows the argument of Cesa-Bianchi et al. (2015) to use the international supply of credit as the measure of global liquidity, and both the long-run effects and the short-run relative strengths of global liquidity on housing prices are analyzed. Hence, this paper uses not only the GIRF to discuss the short-run relative strengths, as with Cesa-Bianchi et al. (2015) but also the panel cointegration of Pedroni (2000, 2004) to identify the long-run linkage between global liquidity and housing prices. Moreover, GVDC was used to estimate the contribution of a global liquidity shock on house price volatility in individual economies, which can confirm that global liquidity innovations are a very important factor in explaining house price volatility in most countries.

The purpose of this chapter is to demonstrate that, despite the extensive literature on firm-specific advantages (FSAs) and country-specific advantages (CSAs) produced since Rugman’s classic matrix (1981), little progress has been made in empirically operationalizing the second concept.

Through a review of the international business (IB) literature that refers to the CSA concept, we identify the “vagueness” in the usage of this concept. First, we present a concise literature review of the CSA construct, with a link to the “double diamond” theoretical model of Rugman and D’Cruz (1993) and Rugman and Verbeke (1993). Second, we present the results of the bibliographic analysis on the use of the construct by a variety of authors.

We demonstrate the weak conceptual grounding of the CSA concept by reviewing the literature on host-CSAs attracting Chinese overseas foreign direct investment (OFDI). Apart from the fact that various authors use different sources of data, an important reason for contradictory results is the fact that each author tests host-CSA through different indicators. Here, we propose a list of variables and indicators based on the “double diamond” model and test these empirically.

IB researchers should start conducting serious studies on home-CSAs and host-CSAs instrumental to attracting investments, defining clear indicators and using replicable data based on publicly available information. This chapter is the first to show that the concepts developed by Rugman (1981) and expanded by Rugman, A. M. and Verbeke, A. (2008) (Internalization theory and its impact on the field of international business. Research in Global Strategic Management, 14, 155–174) are relevant to advance in the quantitative operationalization of concepts within IB theory.